The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review

TL;DR

This systematic review examines how machine learning algorithms are used in recommender systems, highlighting popular methods like Bayesian and decision trees, and identifying research gaps in system development phases.

Contribution

The paper provides a comprehensive analysis of machine learning algorithms in recommender systems and highlights areas for future research in software engineering.

Findings

Bayesian and decision tree algorithms are most commonly used.

Development phases of recommender systems have significant research opportunities.

The review offers insights into current algorithm usage and open challenges.

Abstract

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.